Energy optimizer for dehydrating biofuels through distillation towers and molecular sieves

ABSTRACT

The present invention provides novel techniques for controlling the balance between energy usage and biofuels dehydration between a distillation process unit and molecular sieves with model predictive control technology. In particular, the present techniques are presented in the context of biofuel production, wherein control of the balance between energy usage and water removal in biofuel production may be optimized. However, the present techniques may also be applied to any other suitable applications, such as liquor processing, where energy may be used to remove water from the liquor.

BACKGROUND

The present invention relates generally to control systems, and moreparticularly to model predictive control employing novel techniques foroptimizing energy usage and water removal in a distillation processsystem and a molecular sieve operation.

Many processing applications, such as biofuel production, includesub-processes where energy may be used to remove water from a product,such as biofuel, to meet the moisture specification of the finalproduct. Multiple sub-processes may be used to remove water from theproduct. However, these sub-processes may be characterized by varyingrates of energy usage as well as varying rates of biofuels dehydration.Therefore, balancing biofuels dehydration through multiplesub-processes, such as distillation towers and molecular sieves, is adynamic process control challenge. A main issue in the control of suchsystems may be dehydrating the biofuels product to the commercialspecification of moisture allowable in the product through multiplesub-processes at the minimal energy requirement. However, due tointerdependencies between the sub-processes, as well as particularcharacteristics of each sub-process, such as equipment constraints orthe availability of processing energy in each sub-process, controllingthe balance between energy usage and water removal may be more complexthan simply maximizing the use of the “most efficient” sub-processes.

BRIEF DESCRIPTION

The present invention provides novel techniques for controlling thebalance between energy usage and biofuels dehydration between adistillation process unit and molecular sieves with model predictivecontrol technology. In particular, the present techniques are presentedin the context of biofuel production, wherein control of the balancebetween energy usage and water removal in biofuel production may beoptimized.

In general, the present techniques provide a method for controllingenergy usage and biofuels dehydration in a biofuel production process.The method includes determining energy usage rates of distillationtowers and molecular sieves in the biofuel production process. Themethod also includes determining rates of water removal from biofuel inthe distillation towers and molecular sieves. The method furtherincludes determining target values for operating parameters of thedistillation towers and molecular sieves. In addition, the methodincludes controlling operating parameters of the distillation towers andthe molecular sieves based on the target value determinations.

DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a diagram of an exemplary distillation process productionplant;

FIG. 2 is a more detailed process flow diagram of sub-processes of anexemplary production plant;

FIG. 3 is a flowchart of an exemplary method for integrated modelpredictive control of a biofuel production process;

FIG. 4 is a more detailed process flow diagram of exemplary componentsof the distillation units of FIGS. 1 and 2, illustrating thedistillation/dehydration sub-processes;

FIG. 5 is a process flow diagram of an exemplary dehydration sub-processas performed by the components of the distillation units illustrated inFIG. 4; and

FIG. 6 is a flow chart of an exemplary method for controlling energyusage and water removal in a biofuel production plant.

DETAILED DESCRIPTION

Turning now to the drawings, FIG. 1 is a diagram of an exemplary biofuelproduction plant 10, illustrating how biomass 12 may be processedthrough several stages to produce biofuel 14. Biomass 12 may first beprovided to a milling and cooking process, e.g., milling and cookingunits 16, where water 18 (and possibly recycled water RW1 and RW2) maybe added and the biomass 12 may be broken down to increase the surfacearea-to-volume ratio. This increase in surface area may allow forsufficient interaction of the water 18 and biomass 12 surface area toachieve a solution of fermentable sugars in water 18. The mixture, abiomass 12 and water 18 slurry, may be cooked to promote an increase inthe amount of contact between the biomass 12 and water 18 in solutionand to increase the separation of carbohydrate biomass fromnon-carbohydrate biomass. The output of the milling and cooking units 16(i.e., the fermentation feed or mash) may then be sent to a fermentationprocess, where one or more fermentation vats 20 may operate to fermentthe biomass/water mash produced by the milling and cooking units 16.

The fermentation process may require additional water 18 to control theconsistency of material to the fermentation vats 20 (also referred toherein as a fermenter or fermentation tank). Biomass 12 may be convertedby yeast and enzymes into a biofuel 14 and by-products such as carbondioxide, water and non-fermentable biomass (solids), in the fermentationvats 20. The fermentation process is a batch process and may includemultiple fermenters operating in parallel. The batch start times may bestaggered in order to optimize the utilization of the capacity of thebeer wells 22 and smoothly distribute the flow of fermentation feed tothe fermentation process and the flow of biofuel 14 and stillage asoutput from the fermentation process.

After being temporarily stored in the beer wells 22, the output from thefermentation vats 20 may be sent to a distillation process, e.g., one ormore distillation units 24, to separate biofuel 14 from water 18, carbondioxide, and non-fermentable solids. If the biofuel 14 has to bedehydrated to moisture levels less than 5% by volume, the biofuel 14 maybe processed through a processing unit called a molecular sieve orsimilar processing units (not shown). The finalized biofuel 14 may thenbe processed to ensure it is denatured and not used for humanconsumption.

The distillation units 24 may separate the biofuel 14 from water 18.Water 18 may be used in the form of steam for heat and separation, andthe condensed water may be recycled (RW1) back to the milling andcooking units 16. Stillage 26 (non-fermentable solids and yeastresidue), the heaviest output of the distillation units 24, may be sentto stillage processing units 28 for further development of co-productsfrom the biofuel 14 production process.

The stillage processing units 28 may separate additional water from thecake solids and recycle the water (RW2) back to the milling and cookingunits 16. Several stillage processing options may be utilized,including: (1) selling the stillage with minimal processing and (2)further processing the stillage by separating moisture from the solidproducts via one or more centrifuge units (not shown). Using thecentrifuge units, the non-fermentable solids may be transported todryers (not shown) for further moisture removal. A portion of thestillage liquid (concentrate) may also be recycled back to thefermentation vats 20. However, the bulk of the flow may generally besent to evaporator units (not shown), where more liquid may be separatedfrom the liquid stream, causing the liquid stream to concentrate intosyrup, while solid stillage may be sent to a drying process, e.g., usinga drying unit or evaporator, to dry the solid stillage to a specifiedwater content. The syrup may then be sent to a syrup tank (not shown).Syrup in inventory may be processed using a number of options. Forinstance, the syrup may be: (1) sprayed in dryers to achieve a specifiedcolor or moisture content, (2) added to the partially dried stillageproduct, or (3) sold as a separate liquid product. The evaporator unitsmay have a water by-product stream that is recycled back to the millingand cooking units 16.

An energy center 30 may supply energy to many of the processing units,e.g., the milling and cooking units 16, the distillation units 24 andmole-sieve units, and the stillage processing units 28. The energycenter 30 may constitute a thermal oxidizer unit and heat recovery steamgenerator (HRSG) that may destroy volatile organic compounds (VOCs) andprovide steam to the evaporators, distillation units 24, cooking systemunits (e.g., in 16), and dehydration units. The energy center 30 maytypically be the largest source of heat in a biofuel plant 10.

FIG. 2 is a more detailed process flow diagram of sub-processes of anexemplary biofuel production plant 10. It should be noted that theparticular components, processes and sub-processes shown are merelymeant to be exemplary and are not intended to be limiting. The millingand cooking units 16 may receive water 18, biomass 12, energy(electrical and/or thermal), recycled water, and/or recycled thinstillage, mill the biomass, cook the mixture, and output a biomassslurry (referred to as fermentation feed) to the fermentation process32, which may include the fermentation vats 20 and beer wells 22 shownin FIG. 1. The fermentation process 32 may receive the biomass slurry,water 18, yeast and enzymes 34, and recycled thin stillage, ferment themixture, and output fermentation products to the distillation units 24.The distillation units 24 may receive the fermentation products, removewater and stillage (liquid and solid stillage) from the fermentationproducts in a one- to three-step process (e.g., primary distillationtowers 36, secondary distillation towers 38, and/or molecular sieves(dryers) 40), recycle water removed from the fermentation products tothe milling and cooking units 16, output the liquid and solid stillageto the stillage processing units 28, and output biofuel 14. The stillageprocessing units 28 may receive the liquid and solid stillage, processthe liquid and solid stillage (utilizing one or more of centrifugedryers 42, other dryers 44, and/or evaporators 46) to produce and outputvarious stillage 26, and recycle thin stillage liquid to thefermentation process 32 and the milling and cooking units 16. As in FIG.1 above, the energy center 30 may provide electric power and heat(steam) to the various sub-processes as shown in FIG. 2.

The distillation units 24, which may include primary and secondarydistillation towers 36 and 38, may receive the output of thefermentation process 32 (a mixture of biofuel, stillage, and water) andmay separate the biofuel 14 from the water and stillage. Stillage may beremoved from the primary distillation towers 36 and sent to the stillageprocessing units 28. Energy may be provided to the distillation units 24from the energy center 30 and may be primarily used by one or moreprimary distillation towers 36. The energy may typically be delivered tothe primary distillation towers 36 in the form of a steam flow throughheat exchangers (not shown), but in some embodiments the steam flow maybe added directly to the primary distillation towers 36. Energy may alsobe recycled to the distillation units 24 from other process flows orprovided by other heat sources as needed or desired. The flashedoverhead vapor from the primary distillation towers 36 may betransferred to one or more secondary distillation towers 38 (alsoreferred to as the rectifier and side stripper columns). In thesecondary distillation towers 38, energy may be provided by heatexchangers utilizing steam and/or heat recovery from other processes,such as the milling and cooking units 16 and/or the stillage processingunits 28 utilizing energy recovery streams. The overhead vapor from theprimary distillation towers 36 may be a high-purity biofuel (such as anethanol/water mixture) which may be distilled close to its azeotropicpoint, but generally below fuel specification requirements. The bottomproduct stream of the secondary distillation towers 38 may be primarilycondensed water. This condensed water may be recycled back to themilling and cooking units 16.

The overhead vapor from the primary distillation towers 36 and thesecondary distillation towers 38 may be routed to inventory tanks (notshown) which may be used as surge reservoirs to regulate the feed flowrates between the distillation units and the one or more dehydrationunits. The dehydration units may be molecular sieve units 40 or otherdownstream dehydration processing units (e.g., extractive distillation).Molecular sieve units 40 may include an energy-efficient process unitwhich operates in gas phase using a dehydration process known aspressure swing adsorption (PSA). If the biofuel is ethanol, it may bedehydrated in either the liquid or gas phase. In certain embodiments,molecular sieve units 40 may absorb water in the biofuel vapor such thatthe resulting biofuel 14 may have only a trace amount of water. When themolecular sieve units 40 become saturated with water, they may be takenoffline, replaced with a parallel regenerated unit, and then placed backonline. The offline units may be regenerated under conditions thatrelease moisture and allow the units to dry and be ready for futureonline use. PSA regeneration units may be adjusted to affect theefficiency and capacity of the molecular sieve units 40. The producedbiofuel 14 may then be sent to final storage in product inventory tanks(not shown) and/or directed toward additional processing units.

Equipment for processing stillage may include one or more centrifuges42, one or more evaporators 46, and zero, one, or more dryers 44. Theone or more centrifuges 42 may receive a stillage feed (a mixture ofliquid and solid stillage) from the bottom outputs of the primarydistillation towers 36. The stillage feed from the primary distillationtowers 36 may be routed to inventory tanks (not shown) which may be usedas surge reservoirs to regulate the stillage feed flow rates between theprimary distillation towers 36 and the centrifuges 42. The one or morecentrifuges 42 may separate liquids from the stillage feed, output thethin stillage liquids, and output the remaining solids (dewateredstillage, also referred to as wet cake). The solids (including moistureand non-fermentable solids) may be sent to the dryers 44. Part of thethin stillage liquids may be recycled back to the fermentation process32 and/or the milling and cooking units 16 and the balance may be sentto the one or more evaporators 46 to evaporate moisture from the liquidsto form concentrated syrup. The syrup may be sent to a syrup inventoryunit (not shown) before being combined with the dewatered stillage inthe dryers 44, combined with the dried stillage output from the dryers44, and/or sold as a stand-alone product. The stillage sub-processequipment may also include various heaters (not shown) and combustors(not shown) for the destruction of volatile organic compounds in thevapors from the drying stillage in the one or more evaporators 46 ordryers 44.

One or more of the processes described above may be managed andcontrolled via model predictive control utilizing a dynamic multivariatepredictive model that may be incorporated as a process model in adynamic predictive model-based controller. Model predictive control ofsub-processes in a biofuel production process is described in greaterdetail below. In particular, various systems and methods are providedfor using model predictive control to improve the yield, throughput,energy efficiency, and so forth of biofuel sub-processes in accordancewith specified objectives. These objectives may be set and variousportions of the processes controlled continuously to provide real-timecontrol of the production process. The control actions may be subject toor limited by plant and external constraints.

Each of the illustrated sub-processes may operate within the largerbiofuel production process to convert biomass 12 to biofuel 14 andpossibly one or more co-products. Thus, the biofuel production plant 10may typically include four general plant sections: milling/cooking,fermentation, distillation/sieves, and stillage processing. Each ofthese sub-processes may be at least partially dependent upon operationof one or more of the other sub-processes. Moreover, operatingconditions that may be optimal for one sub-process may entail or causeinefficiencies in one or more of the other sub-processes. Thus, a plantbottleneck, meaning a local limitation that limits or restricts a globalprocess, may occur in any of the above four sub-processes, thus limitingthe overall operation of the biofuel production plant 10.

Thus, an operating challenge for biofuel production is to manage thevarious sub-processes, and possibly the entire system or process, toautomatically respond to a constraint or disruption in the productionsystem or process. As described in greater detail below, integratedmodel predictive control may be used to manage the biofuel productionprocess in a substantially optimal manner, balancing various, andpossibly competing, objectives of the sub-processes to approach, meet,and/or maintain objectives for the overall process. More specifically,the disclosed embodiments of model predictive control may be used tomanage the balance between energy usage and water removal in thedistillation process.

The control of these sub-processes may be performed manually, e.g.,based on decisions of a human operator, or may only be locallyautomated, e.g., via proportional-integral-derivative (PID) inventorycontrols of fermentation inventory and fermentation feed inventory.However, given the complexity of the relationships among the manyfactors or variables, such manual control generally results insignificant inefficiencies, sub-optimal yields, etc.

FIG. 3 is a flowchart of an exemplary method 48 for such integratedmodel predictive control of a biofuel production process. Morespecifically, embodiments of the method 48 may apply model predictivecontrol techniques to manage multiple sub-processes of the biofuelproduction process in an integrated manner. Note that in variousembodiments, many of the method steps may be performed concurrently, ina different order than shown, or may be omitted. Additional method stepsmay also be performed.

In step 50, an integrated dynamic multivariate predictive modelrepresenting a plurality of sub-processes of the biofuel productionprocess may be provided. In other words, a model may be provided thatspecifies or represents relationships between attributes or variablesrelated to the sub-processes, including relationships between inputs tothe sub-processes and resulting outputs of the sub-processes.

The model may be of any of a variety of types. For example, the modelmay be linear or nonlinear, although for most complex processes, anonlinear model may be preferred. Other model types contemplated includefundamental or analytical models (i.e., functional physics-basedmodels), empirical models (such as neural networks or support vectormachines), rule-based models, statistical models, standard modelpredictive control models (i.e., fitted models generated by functionalfit of data), or hybrid models using any combination of the abovemodels.

The integrated dynamic multivariate predictive model may include a setof mathematical relationships that includes steady state relationshipsand may also include the time lag relationship for each parameter changeto be realized in the output. A great variety of dynamic relationshipsmay be possible and each relationship between variables may characterizeor capture how one variable may affect another and also how fast theeffects may occur or how soon an effect may be observed at anotherlocation.

The integrated dynamic multivariate predictive model may be created froma combination of relationships based on available data such asfundamental dynamic and gain relationships, available plant historicprocess data, and supplementary plant testing on variables that may notbe identified from the two previous steps. Models may be customized tothe plant layout and design, critical inventories, plant constraints andmeasurements, and controllers available to manage variables. Moreover,in some embodiments, external factors, such as economic or regulatoryfactors, may be included or represented in the model.

An important characteristic of the integrated dynamic multivariatepredictive model may be to identify when a control variable changes as aresult of a change in one or more manipulated variables. In other words,the model may identify the time-response (e.g., time lag) of one or moreattributes of a sub-process with respect to changes in manipulatedvariables. For example, once a controller adjusts pump speeds, there maybe a certain time-dependent response before observing an effect at atank being filled. This time-dependent response may be unique for eachindependent controller. For instance, flow rates may vary because ofdifferences in system variables (e.g., piping lengths, tank volumes, andso forth) between the control actuator and sensor and the pump location.

With respect to the distillation/dehydration sub-processes discussedabove, distillation feed tank levels and individual feeds todistillation units may be managed through calculations of the integrateddynamic multivariate predictive model. However, there may be otherprocess disturbances that may remain unmeasured. For example, asituation may occur where a tank level starts to rise out of balancewith filling demand (e.g., because of manual plant changes such asscheduled equipment cleaning that involves draining and/or filling oneor more specific tanks). In this situation, the integrated dynamicmultivariate predictive model may be made aware of the imbalance so thatcorrective actions may be made gradually to avoid dramatic or criticalconsequences. This may, for instance, be an issue for many of the tanksthat have both batch and continuous plant operations in sequence.Specific tanks may be used to provide storage capacity to facilitatebalancing and avoid continuous out-of-control operations after everybatch action. Because batch vessels may drain rapidly, specific tanklevels may be difficult to maintain in automatic level control. Thus,real-time receipt of current vessel and material balance information(flows and levels) may provide an update on current equipment status andthe execution of the integrated dynamic multivariate predictive modelmay enable projections to be made to avoid both emptying/over-fillingvessels and large emergency flow moves to correct imbalances.

In certain embodiments, the integrated dynamic multivariate predictivemodel may include inferential models (also referred to as propertyapproximators or virtual online analyzers (VOAs)). An inferential modelmay be a computer-based model which calculates inferred qualityproperties from one or more inputs of other measured properties (e.g.,process stream or process unit temperatures, flows, pressures,concentrations, levels, and so forth). For example, in one embodiment,these inferential models may compute the real-time properties of one ormore properties from a list of properties comprising primarydistillation tower 36 biofuel concentration in the bottom productstream, secondary distillation tower 38 biofuel concentration in theoverhead product stream, secondary distillation tower 38 biofuelconcentration in the bottom product stream, product stream off themolecular sieve units 40, and/or product stream quality off anextractive distillation, among others. In certain embodiments, theintegrated dynamic multivariate predictive model may be subdivided intodifferent portions and stored in a plurality of memories. The memoriesmay be situated in different locations of the biofuel production plant10. The controller may communicate with the memories utilizing acommunication system.

In step 52, a specified objective for the plurality of sub-processes maybe received. The objective may specify a desired behavior or outcome ofthe biofuel production process. In certain embodiments, the objectivemay be somewhat complex or compound. For example, the objective mayinclude a global objective and a plurality of sub-objectives directed toat least a subset of the plurality of sub-processes. In other words, thespecified objective may include an overall objective for the biofuelproduction process, e.g., maximize throughput, efficiency, and so forth,and may also include various subsidiary objectives related specificallyto the respective sub-processes. In addition, the sub-objectives may bemutually exclusive or competitive with respect to each other and/or withrespect to the global objective.

Exemplary objectives may include, but are not limited to, one or moreoperator specified objectives, one or more predictive model specifiedobjectives, one or more programmable objectives, one or more target feedrates, one or more cost objectives, one or more quality objectives, oneor more equipment maintenance objectives, one or more equipment repairobjectives, one or more equipment replacement objectives, one or moreeconomic objectives, one or more target throughputs for the biofuelproduction process, one or more objectives in response to emergencyoccurrences, one or more dynamic changes in materials inventoryinformation, one or more dynamic changes in available process energyconstraints, or one or more dynamic changes in one or more constraintson the biofuel production process, and so forth.

With respect to the distillation/dehydration sub-processes, theobjectives may be specified by a human operator and/or a program, and insome embodiments the objectives may include one or more sub-objectives.The objectives may include one or more of combined feed rate to theprimary distillation towers 36, individual feed rates to each primarydistillation tower 36, heating load of the primary distillation towers36, flow rate of non-fermentable solids output, rate of loss of biofuelinto the non-fermentable solids output from the primary distillationtowers 36, distillation base ethanol concentration of output of theprimary distillation towers 36, water content of the biofuel stream offthe secondary distillation towers 38, rate of loss of biofuel incondensed water output from the secondary distillation towers 38, watercontent in one or more output biofuel products, flow rates andinventories of one or more output biofuel products, and/or purityspecification of one or more output biofuel products. In particular, incertain embodiments, a specific objective may include the maximizationof water removal in the distillation/dehydration sub-processes and theminimization of energy usage within these sub-processes.

In step 54, process information related to the plurality ofsub-processes may be received from the biofuel production process. Thisprocess information may be any type of process information, includingstate or condition information measured by sensors (e.g., temperatures,pressures, real-time measurements of the biofuel in the fermentationsystem, and so forth), computed algorithmically, inferred from models(i.e., inferential models), taken from lab values, entered by operators,and so forth. The process information may further include equipmentsettings, flow rates, material properties (e.g. densities), contentprofiles, purity levels, ambient conditions (e.g., time of day,temperature, pressure, humidity, and so forth), economic or marketconditions (e.g., cost of materials or product), and so forth. In otherwords, the process information may include any information that affectsor influences any part of the biofuel production process.

More specifically, the process information may include measurements ofone or more control variables and one or more manipulated variablesrelated to the sub-processes and one or more variables of otherprocesses that may impact the sub-processes, as well as information frominferential models, laboratory results, and so forth. With respect tothe distillation/dehydration sub-processes discussed above, the measuredvariables may include distillation unit feed rates; distillation feedtemperatures; heat input to the primary distillation towers 36; heatinput to the secondary distillation towers 38; heat input to thedehydration units; output flow rate of non-fermentable solids; the lossof biofuel into stillage (which may be the product from the bottom ofthe primary distillation towers 36); the water content of the biofuelstream off the secondary distillation towers 38; the loss of biofuel tothe secondary distillation towers 38 bottom product stream; columnreflux of the distillation units 24; pump speed, valve position, orother controller output within the distillation or dehydration systems;pressure drop within the distillation section or piping sections; columnpressure; distillation base biofuel concentration of output of primarydistillation units 36; biofuel product composition from one or moreprimary distillation towers 36, biofuel product composition from one ormore secondary distillation towers 38; biofuel product composition fromone or more dehydration units; process heating limits of thedistillation/dehydration process units; pressure limits of thedistillation/dehydration process units; pressure drop limitation of thevaporized feed in the dehydration units; limits of the dehydration feedsystems; water content of the one or more output biofuel products;purity specification of one or more output biofuel products; and/or theinventory of one or more output biofuel products, among others. Theprocess information may be communicated to the controller from adistributed control system.

In step 56, the integrated dynamic multivariate predictive model may beexecuted in accordance with the objective using the received processinformation as input, thereby generating model output comprising targetvalues of one or more controlled variables related to one or more of theplurality of sub-processes in accordance with the objective. In otherwords, the model may be executed to determine target values formanipulated variables for one or more of the sub-processes that may beused to control the sub-processes in such a way as to attempt to meetthe specified objective.

For example, in an embodiment where the objective is to maximize waterremoval in the distillation/dehydration sub-process, the model maydetermine various target values (e.g., sub-process material input flows,temperatures, pressures, and so forth) that may operate to maximize thewater removal. As another example, in an embodiment where the objectiveis to minimize energy usage for the distillation/dehydrationsub-process, the model may determine target values that may operate tominimize energy usage of the distillation/dehydration sub-process,possibly at the expense of water removal of the distillation/dehydrationsub-process. In a further example, the objective may be to maximizeprofit for the entire biofuel production process, where maximizing waterremoval and minimizing energy usage may be two, possibly competing,sub-objectives, e.g., included in the objective.

It should be noted that as used herein, the terms “maximum,” “minimum,”and “optimum,” may refer respectively to “substantially maximum,”“substantially minimum,” and “substantially optimum,” where“substantially” indicates a value that is within some acceptabletolerance of the theoretical extremum, optimum, or target value. Forexample, in one embodiment, “substantially” may indicate a value within10% of the theoretical value. In another embodiment, “substantially” mayindicate a value within 5% of the theoretical value. In a furtherembodiment, “substantially” may indicate a value within 2% of thetheoretical value. In yet another embodiment, “substantially” mayindicate a value within 1% of the theoretical value. In other words, inall actual cases (non-theoretical), there are physical limitations ofthe final and intermediate control element, dynamic limitations to theacceptable time frequency for stable control, or fundamental limitationsbased on currently understood chemical and physical relationships.Within these limitations, the control system will generally attempt toachieve optimum operation, i.e., operate at a targeted value orconstraint (maximum or minimum) as closely as possible.

In step 58, the plurality of sub-processes of the biofuel productionprocess may be controlled in accordance with the target values and theobjective. In other words, a controller (or a plurality of controllers)may modulate or otherwise control various operational aspects of thesub-processes in accordance with the target values of the manipulatedvariables. In some embodiments, the target values may simply be used asset points by the controller. In other words, the controller may setrespective inputs of the various sub-processes to the respective targetvalues. For example, controlling the plurality of sub-processes of thebiofuel production process in accordance with the target values and theobjective may include operating one or more controllers to control oneor more of the following: one or more material feed rates, one or morewater flows, one or more molecular sieve regenerations, one or more heatsources, and so forth.

With respect to the distillation/dehydration sub-processes discussedabove, controlling the biofuel production process may includecontrolling, among other things, the flow rates of the distillationfeed, the primary distillation tower 36 heat balance, the loss ofbiofuel into stillage, the water content of biofuel from the secondarydistillation towers 38, the loss of biofuel to the secondarydistillation tower 38 bottom product stream, the inventory of biofuel,the biofuel moisture quality, and so forth.

Steps 52, 54, 56, and 58 of the method 48 may be performed a pluralityof times in an iterative manner to operate the biofuel productionprocess in a substantially optimal fashion. In other words, the method48 described above may be performed substantially continuously, such asat a specified frequency, providing control of the biofuel productionprocess in substantially real time to optimize the biofuel productionprocess with respect to the specified objective.

In embodiments where multiple objectives may be provided, possibly atodds with one another, an optimizer may be used to balance the varioussub-objectives in attempting to meet the global objective. In otherwords, an optimizer may be used to determine how to compromise withrespect to the various sub-objectives in attempting to achieve theglobal objective. Thus, in certain embodiments, executing the integrateddynamic multivariate predictive model may include an optimizer executingthe integrated dynamic multivariate predictive model to generate themodel output. The generated model output may include the target valuesof one or more variables related to one or more of the plurality ofsub-processes in accordance with the global objective and the pluralityof sub-objectives. In certain embodiments, the optimizer may execute theintegrated dynamic multivariate predictive model a plurality of times inan iterative manner. For example, the optimizer may repeatedly executethe model using various inputs and compare the resulting outputs to thespecified objective (including the sub-objectives), thereby searchingthe solution space for input configurations that optimize the outcome,e.g., that allow the global objective to be met or at least approached,while minimizing the compromises made with respect to the varioussub-objectives.

In certain embodiments, the method 48 may further include receivingconstraint information specifying one or more constraints, such aslimitations on one or more aspects or variables of the biofuelproduction process. The optimizer may execute the integrated dynamicmultivariate predictive model in accordance with the objective using thereceived process information and the one or more constraints as input,thereby generating the model output in accordance with the objective andsubject to the one or more constraints. The one or more constraints mayinclude any such limitation on the biofuel production process including,but not limited to, one or more of: batch constraints (e.g.,fermentation time), water constraints, feed constraints, equipmentconstraints, capacity constraints, temperature constraints, pressureconstraints, energy constraints, market constraints, economicconstraints, environmental constraints, legal constraints,operator-imposed constraints, and so forth. Furthermore, examples ofequipment constraints may include, but are not limited to, one or moreof: operating limits for pumps, operational status of pumps, tankcapacities, operating limits for tank pressures, operational status oftanks, operating limits for valve pressures, operating limits for valvetemperatures, operating limits for pipe pressures, operating limits forenergy provision, operating limits for molecular sieves, and so forth.Moreover, in certain embodiments, the constraint information may includedynamic constraint information. In other words, some of the constraintsmay change dynamically over time. Therefore, the method 48 mayautomatically adjust operations taking into account these changingconstraints.

In certain embodiments, the system may derive its measurements orprocess information from the process instruments or sensors, inferentialmodels, real-time measurements of the biofuel in the fermentationsystem, and/or lab values, and execute linear or non-linear dynamicprediction models to solve an overall optimization objective which maytypically be an economic objective function subject to dynamicconstraints of the plant processes. The system may then execute theintegrated dynamic multivariate predictive model, controller, andoptimizer in accordance with the objective, e.g., the optimizationfunction. For instance, the system may optimize water removal of thedistillation/dehydration sub-processes with energy usage of thedistillation/dehydration sub-processes.

FIG. 4 is a more detailed process flow diagram of exemplary componentsof the distillation units 24 of FIGS. 1 and 2, illustrating thedistillation/dehydration sub-processes. As described above, a primaryfunction of the distillation units 24 is to separate biofuels fromwater. The heaviest product of the distillation units 24 is stillagewhich may be sent to the stillage processing units 28 (not shown in FIG.4). The primary distillation tower, otherwise known as the beer column60, is where the main source of energy for distillation may be added.The energy may often be evaporator steam but may also be any otherheating media. The beer column 60 may receive fermentation product fromthe beer wells 22 and use the energy source to generate stillage, whichmay be sent to the whole stillage tanks. The beer column 60 may alsogenerate a flashed vapor which may be directed into the secondarydistillation towers and, more specifically, into a rectifier column 62.A separate energy source may not actually be applied at the rectifiercolumn 62. Rather, the rectifier column 62 may function as a holdingcolumn which may circulate the flashed vapor between itself and a sidestripper column 64. External energy sources (e.g., cook flash steam fromthe milling and cooking sub-processes, clean feed steam, and so forth)may be used by the side stripper column 64 to help separate condensedwater from the flashed vapor. The condensed water may be recycled to themilling and cooking sub-processes. The overhead product from therectifier and side stripper columns 62, 64 is often a high-puritybiofuel which may be distilled close to its azeotropic point.

The high-purity biofuel from the rectifier column 62 may be directedthrough overhead condensers 66 and reflux drums 68 into the 190-proofinventory tanks 70. A certain amount of reflux may flow back into therectifier column 62. The 190-proof inventory tanks 70 may be used as asurge reservoir to allow constant feed through sieve vaporizers 72 andinto the molecular sieves 40. Within the molecular sieves 40, thebiofuel may be dehydrated in either the liquid or gas phase. Perhaps themost energy-efficient method is to dehydrate the biofuel in the gasphase using the PSA techniques described above. Using these techniques,water may be absorbed into the molecular sieve 40 beds, therebydehydrating the biofuel to a point where only a trace amount of waterremains. When a set of molecular sieve 40 beds become saturated withwater, they may be taken offline and a fresh set of molecular sieve 40beds may be placed online. The offline beds may be regenerated underconditions that may release the moisture and allow the beds to dry andbecome ready for subsequent online use. PSA regeneration times may beadjusted in order to adjust the bed efficiency. From the molecularsieves 40, the biofuel product may be sent to the final storage tanks74, where the biofuel may be held for final storage and processing.

As shown in FIG. 4, various components of the distillation/dehydrationsub-processes may contribute to not only the distillation of biofuelfrom the fermentation product, but also the removal of water from theproduced biofuel. FIG. 5 is a process flow diagram of an exemplarydehydration sub-process as performed by the components of thedistillation units 24 illustrated in FIG. 4. As described above,fermentation output from the fermentation process 32 of FIG. 2 may bedirected into the distillation towers (e.g., the beer column 60, therectifier column 62, and the side stripper column 64). Within thedistillation towers 60, 62, 64, an energy source (e.g., steam) may beused to remove water (as well as stillage) from the fermentationproduct, thereby producing a high-purity biofuel. From the distillationtowers 60, 62, 64, the high-purity biofuel may be directed into themolecular sieves 40, where additional energy sources may be used tofurther remove water, thereby producing a biofuel product.

Therefore, the dehydration sub-process may be performed primarily by twodiscrete sub-systems, the distillation towers 60, 62, 64 and themolecular sieves 40. However, the distillation towers 60, 62, 64 mayonly be capable of removing so much water due in part to the chemicalbonding of ethanol and water and may, in fact, allow for water contentsin the biofuel as high as 5-20%. In addition, the high-pressuredistillation may be more expensive and may consume more energy than themolecular sieves 40. Therefore, in some instances, a decision may bemade to separate less water using the distillation towers 60, 62, 64 andto allow the molecular sieves 40 to remove the remaining water from thebiofuel. However, such a strategy may lead to too much water beingdirected into the molecular sieves 40 such that the molecular sieves 40may become saturated. As such, a trade-off may be made to balance theenergy usage between the distillation units 60, 62, 64 and the molecularsieves 40 to generate biofuels with as low water content levels aspossible without adversely affecting the molecular sieves 40 as well asother components of the dehydration sub-process.

Using the model predictive control techniques discussed in greaterdetail above, it may be possible to monitor, control, and optimize thesub-processes (in particular, the distillation/dehydrationsub-processes) of the biofuel production process in order to implementoptimized decisions regarding the trade-off between energy usage and therate of water removal from the produced biofuels. For instance, FIG. 6is a flow chart of an exemplary method 76 for controlling energy usageand water removal in a biofuel production plant 10. The method 76 may beintegrated into the model predictive control method 48 of FIG. 3 above.Indeed, the method 76 may be one exemplary embodiment of the predictivemodel techniques discussed above.

In step 78, model relationships of (1) energy per unit mass or energyper unit volume of throughput and (2) energy per unit moisture removalof one or more distillation towers 60, 62, 64 may be determined. Thesemodel relationships may be determined using any of the techniquesdescribed above and may include, for instance, measuring the modelrelationships using process instruments. However, in many instances, themodel relationships may not be easily measurable. Therefore, proxyvalues may be determined, for instance, based on inferential models andexternal calculations. For example, these model relationships may bedetermined based on measured input values such as steam consumption ofthe one or more distillation towers 60, 62, 64, steam temperaturecontroller readings, energy and mass balance calculations, and so forth.In step 80, constraints of the one or more distillation towers 60, 62,64 may also be determined. These constraints may include, but are notlimited to, process constraints, energy constraints, equipmentconstraints, legal constraints, operator-imposed constraints, and soforth.

In step 82, model relationships of (1) energy per unit mass or energyper unit volume of throughput and (2) energy per unit moisture removalof one or more molecular sieves 40 may be determined. These modelrelationships may, again, be determined using any of the techniquesdescribed above and may include, for instance, measuring the modelrelationships using process instruments. However, again, the modelrelationships may not be easily measurable. Therefore, proxy values mayalso be determined, for instance, based on inferential models andexternal calculations. In step 84, constraints of the one or moremolecular sieves 40 may also be determined. These constraints may alsoinclude, but are not limited to, process constraints, energyconstraints, equipment constraints, legal constraints, operator-imposedconstraints, and so forth.

In step 86, the economic cost of energy utilized within the one or moredistillation towers 60, 62, 64 and the one or more molecular sieves 40may optionally be determined. In addition, in step 86, the economicvalue of biofuel products may also optionally be determined. Theseeconomic cost and value determinations may optionally be used in theoptimal target value determination step 88.

In step 88, optimal target values for a distillation product moisturespecification may be determined. This determination of optimal targetvalues may take into account the model relationships, constraints, andeconomic cost and value determinations discussed above with respect tosteps 78, 80, 82, 84, and 86 of the method 76.

Then, in step 90, operating parameters of the one or more distillationtowers 60, 62, 64 and the one or more molecular sieves 40 may becontrolled based on the optimal target value determinations from step88. The control of operating parameters of the one or more distillationtowers 60, 62, 64 and the one or more molecular sieves 40 may includemanipulation of any number of the process variables described above. Forexample, control of the one or more distillation towers 60, 62, 64 mayinclude control of feed flow trajectories, syrup evaporation steamtrajectories, overhead pressures, rectifier column 62 reflux, sidestripper column 64 steam, valve settings, and so forth. Similarly,control of the one or more molecular sieves 40 may include control of190-proof moisture, molecular bed cycle time, sieve feed rates, sieveinlet temperature and flow rates, sieve back pressure, and so forth.

The steps 78, 80, 82, 84, 86, 88, and 90 of the method 76 may becyclically repeated during the biofuel production process. In addition,steps 78, 80, 82, 84, 86, 88, and 90 of the method 76 may be performedsequentially, simultaneously, or in any order relative to one another.

The control systems used to implement the present techniques may be openor closed. Open loop systems are only defined by the inputs and theinherent characteristics of the system or process. In the biofuelproduction process, the system may be the entire biofuel productionplant, one sub-process of the biofuel production plant, such as themilling and cooking units 16, or control of a variable in a process suchas the temperature of the milling and cooking units 16. In a closed loopsystem, the inputs may be adjusted to compensate for changes in theoutput where, for example, these changes may be a deviation from desiredor targeted measurements. A closed loop system may sense a change andprovide a feedback signal to a process input. Process units in thebiofuel production system may be closed loop systems if they need to beregulated subject to constraints such as product quality, energy costs,process unit capacity, and so forth. Traditional PID controllers andother control systems such as ratio controls, feed-forward controls, andprocess models may be used to control biofuel production processes. Adistributed control system may have many control schemes set up tocontrol the process unit variables at the local control level.

The control systems may include a computer system with one or moreprocessors, and may include or be coupled to at least one memory medium(which may include a plurality of memory media), where the memory mediummay store program instructions according to the present techniques. Invarious embodiments, controllers may be implemented on a single computersystem communicatively coupled to the biofuel production plant 10, ormay be distributed across two or more computer systems, e.g., that maybe situated at more than one location. In this embodiment, the multiplecomputer systems comprising the controllers may be connected via a busor communication network.

The automated control system for the biofuel production plant 10 mayinclude one or more computer systems which interact with the biofuelproduction plant 10 being controlled. The computer systems may representany of various types of computer systems or networks of computer systemswhich execute software programs according to various embodiments of thepresent techniques. The computer systems may store (and execute)software for managing sub-processes in the biofuel production plant 10.The software programs may perform various aspects of modeling,prediction, optimization and/or control of the sub-processes. Thus, theautomated control system may implement predictive model control of thesub-processes in the biofuel production plant 10. The system may furtherprovide an environment for making optimal decisions using anoptimization solver (i.e., an optimizer) and carrying out thosedecisions (e.g., to control the plant).

One or more software programs that perform modeling, prediction,optimization and/or control of the biofuel production plant 10 may beincluded in the computer systems. Thus, the systems may provide anenvironment for a scheduling process of programmatically retrievingprocess information relevant to the sub-processes of the biofuelproduction plant 10, and generating actions to control thesub-processes, and possibly other processes and aspects of the biofuelproduction plant 10.

The computer systems may preferably include a memory medium on whichcomputer programs according to the present techniques may be stored. Theterm “memory medium” is intended to include various types of memory orstorage, including an installation medium (e.g., a CD-ROM or floppydisks), a computer system memory or random access memory (e.g., DRAM,SRAM, and so forth), or a non-volatile memory such as a magnetic medium(e.g., a hard drive or optical storage). The memory medium may compriseother types of memory as well, or combinations thereof. In addition, thememory medium may be located in a first computer in which the programsare executed, or may be located in a second different computer whichconnects to the first computer over a network. In the latter instance,the second computer may provide the program instructions to the firstcomputer for execution.

Also, the computer systems may take various forms, including a personalcomputer system, mainframe computer system, workstation, networkappliance, Internet appliance or other device. In general, the term“computer system” may be broadly defined to encompass any device (orcollection of devices) having a processor (or processors) which executesinstructions from a memory medium. The memory medium (which may includea plurality of memory media) may preferably store one or more softwareprograms for performing various aspects of model predictive control andoptimization. A CPU, such as the host CPU, executing code and data fromthe memory medium may include a means for creating and executing thesoftware programs.

The present techniques have been presented in the context of optimizingthe control of energy usage rates and rates of water removal withrespect to the production of biofuels. However, the present techniquesmay also be applied to any other systems where energy may be used toremove water from a product and where there may inherently be atrade-off between the energy usage and water removal rates from multiplesub-processes. In other words, any system where water may be removedfrom a product of the system using multiple sub-processes with varyingenergy usage rates may utilize the present techniques. For instance, theprocessing of liquor may be another application where the presenttechniques may be used. During the distillation sub-process, water willbe removed as part of the distillation. However, if the liquor beingproduced will be of a higher proof, further water removal may beperformed. Whether molecular sieves are used as the second water removalsub-process may depend on the specific implementation. However, thesecond water removal sub-process may be characterized by differentenergy usage rates than the distillation sub-process as well as otherparticular aspects which may be considered by the predictive model-basedtechniques described herein to determine appropriate target values foroperating parameters of the sub-processes.

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

The invention claimed is:
 1. A non-transitory computer readable medium,comprising: computer code programmed on the non-transitory computerreadable medium, wherein the code comprises instructions for optimizingenergy usage rates and rates of water removal in a production system,the instructions comprising: instructions for determining a modelrelationship of energy per unit mass or energy per unit volume ofthroughput of at least one distillation tower, and determining a modelrelationship of energy per unit moisture removal of the at least onedistillation tower; instructions for determining constraints of the atleast one distillation tower; instructions for determining a modelrelationship of energy per unit mass or energy per unit volume ofthroughput of at least one molecular sieve, and determining a modelrelationship of energy per unit moisture removal of the at least onemolecular sieve; instructions for determining constraints of the atleast one molecular sieve; instructions for executing an integrateddynamic multivariate predictive model to determine optimal target valuesfor a distillation product moisture specification based on thedetermined model relationships and constraints; and instructions fordynamically controlling operating parameters of the at least onedistillation tower and operating parameters of the at least onemolecular sieve based on the optimal target value determinations,wherein the instructions for dynamically controlling the operatingparameters include instructions for determining a balance betweenseparating water from a biofuel product using the at least onedistillation tower and removing water from the biofuel product using theat least one molecular sieve; wherein the recited instructions arecyclically executed during operation of the production system.
 2. Thenon-transitory computer readable medium of claim 1, wherein the codecomprises instructions for determining an economic cost of energyutilized within the at least one distillation tower and the at least onemolecular sieve and instructions for determining the economic value ofbiofuel products.
 3. The non-transitory computer readable medium ofclaim 2, wherein the instructions for determining optimal target valuesfor the distillation product moisture specification are also based onthe economic cost and economic value determinations.
 4. Thenon-transitory computer readable medium of claim 1, wherein the codecomprises instructions for measuring the model relationship of energyper unit mass or energy per unit volume of throughput of the at leastone distillation tower using process instruments, and instructions formeasuring the model relationship of energy per unit moisture removal ofthe at least one distillation tower using process instruments.
 5. Thenon-transitory computer readable medium of claim 1, wherein the codecomprises instructions for measuring the model relationship of energyper unit mass or energy per unit volume of throughput of the at leastone molecular sieve using process instruments, and instructions formeasuring the model relationship of energy per unit moisture removal ofthe at least one molecular sieve using process instruments.
 6. Thenon-transitory computer readable medium of claim 1, wherein thenon-transitory computer readable medium is disposed in a processcontroller for controlling energy usage and water removal in a biofuelproduction plant.